---
title: "Fit or Frite?"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    logo: http://52.214.106.253/img/fit36.png
editor_options: 
  chunk_output_type: console
---


```{r include=FALSE}
library(jsonlite)
library(data.table)
library(hms)
library(data.table)
library(plotly)
library(knitr)

knitr::opts_chunk$set(include = FALSE, echo = FALSE)

randomize_name <- function(x){
  n <- length(unique(x))
  r <- randomNames::randomNames(n, which.names = "first", sample.with.replacement = FALSE)
  r[as.integer(as.factor(x))]
}


```
```{r prep ilog}
act <- readRDS("/mnt/s3/ilog/activities.rds")
act[, respondent_id := respondent]
act[, respondent := randomize_name(respondent)]


act[, sporty_type := grepl("sport", type, ignore.case = TRUE)]
act[, sporty_mot  := grepl("(foot)|(bicycle)", type, ignore.case = TRUE)]

act[, time := as.hms(ts_notif)]
act

ppl <- act[, .(
  sports = sum(sporty_type | sporty_mot), 
  answers = sum(type != "Expired"),
  sleep   = sum(type == "Sleeping"),
  days = difftime(max(ts_answer), min(ts_notif), unit = "days")
), 
  by = "respondent"
]

ppl[, fitness := sports/as.numeric(days)]
ppl[, sleepy  := sleep/as.numeric(days)]
ppl[, rigor   := answers/as.numeric(days)]

ppl[, fitlvl := dplyr::case_when(
  fitness == 0 ~ 0,
  fitness <  0.5 ~ 1,
  TRUE ~ 2
)]


ppl <- ppl[rigor > 12 & days >= 1]
act <- act[respondent %in% ppl$respondent]

```





### intro

* Sleep
* Food
* Ftiness Level

### iLog - Sleep

```{r include=TRUE}

data.table::setorderv(ppl, "sleepy")

order <- list(
  categoryorder = "array",
  categoryarray = ppl$respondent[order(ppl$sleepy)]
)


ppl[, fitfct := as.factor(fitlvl)]
levels(ppl$fitfct) <- c("easy-going", "average", "fit")

plot_ly(
  data = ppl,
  legendgroup = ~fitfct
) %>% 
  plotly::add_markers(x = ~respondent,
  y = ~sleepy,
  type = "scatter",
  color = ~fitfct) %>% 
  layout(xaxis = order, yaxis = list(title = "hours of sleep", range = c(0, 10))) %>% 
  add_segments(x = ~respondent, xend =  max(ppl$respondent), y = 5, yend = 5, showlegend = FALSE)

```

***

The average sleeping time of iLog users was 5 hours. 


### Logging activities with iLog

```{r include = TRUE}
knitr::include_graphics("http://52.214.106.253/img/bad_sleep.jpeg")
# /usr/share/nginx/html
```

***
The user has to fill out data every hour. 
For long monotonous activities like sleeping,
this can become very tedious.


### How iLog should ask

```{r include = TRUE}
knitr::include_graphics("http://52.214.106.253/img/good_sleep.jpeg")
```

***
Long periods of no activities can easily be identified with sensor
data.  No complicated calculations are necessary to identified
periods of no activity inside the phone, just monitoring of
the sensors


### iLog Sensors

```{r sensor meta}

x <- fread("/mnt/s3/ilog/meta.csv")

x[, respondent_id := strtrim(respondent, 4)]
x[, respondent := respondent_id]
x[, respondent := act$respondent[match(x$respondent, act$respondent_id)]]
x[is.na(respondent), respondent := randomize_name(respondent_id)]

data.table::setnames(x, names(x), gsub(".parquet", "", names(x)))
m <- as.matrix(x[, !c("respondent", "respondent_id", "sensors")], rownames = x$respondent)
d <- as.integer(m > 0)
dim(d) <- dim(m)
colnames(d) <- colnames(m)
rownames(d) <- rownames(m)
d <- t(d)
d <- d[order(rowSums(d)), ]
d <- d[, rev(order(colSums(d)))]
colnames(d) <- strtrim(colnames(d), 6)
```
```{r heatmap sensor meta, include=TRUE, echo=FALSE}
plot_ly(x=seq_along(colnames(d)), y=rownames(d), z = d, type = "heatmap", showscale = FALSE, colors = c("#66c2a4", "#238b45"))
```

***

Not all sensors available on all phones, and not all of them
are useful for evaluation sleeping patterns. The following
sensors could be especially useful to identify sleep periods 
and wake-up events:

* accelerometerevent
* screenevent
* orientationevent
* rotationvectorevent
* linearaccelerationevent
* gravityevent
* touchevent
* gyroscopeevent
* headsetplugevent
* airplanemodeevent
* (time)

### iLog sensible sensors

```{r}
sleep_events <-
  c(
    "accelerometerevent",
    "screenevent",
    "orientationevent",
    "rotationvectorevent",
    "linearaccelerationevent",
    "gravityevent",
    "touchevent",
    "gyroscopeevent",
    "headsetplugevent",
    "airplanemodeevent"
  )


d[rownames(d) %in% sleep_events] <- d[rownames(d) %in% sleep_events] + 2
```

```{r heatmap sensible sensors, include=TRUE, echo=FALSE}
plot_ly(x=seq_along(colnames(d)), y=rownames(d), z = d, type = "heatmap", showscale = FALSE, colors = c("#edf8fb", "#b2e2e2", "#66c2a4", "#238b45")) 
```

***

Long periods of inactivy at night indicate sleep

***

```{r include = TRUE, fig.width = 1}
knitr::include_graphics("http://52.214.106.253/img/sleep.svg")
```

### Meals



***

Different eating patterns can be explored via a trelloscope widget. The
filtering can be used to limit the pictures according to several characteristics

#### fitness

* **fitness** of the responent modeled with the number of
  biking, walking and jogging minutes
* median number of **steps** per day

#### properties of the meal

- does the meal/drink/snack contain **sugar**?
- Is it **home prepared**?
- What **temperature** is it?
- **meal type** (breakfast, lunch, dinner, drink)

#### other

- When (**hour**, **day**) and where (**country**) was the meal consumed?

### Geographical aspect

```{r include = TRUE, fig.width = 1}
knitr::include_graphics("http://52.214.106.253/img/fitness_cat_country_2.jpg")
```

***

The geographical distribution of fitness among the EU countries can be displayed 
with the geofacet package. 


The histograms in the plot can easily be replaced by other plot types.